ALBERT

Description: © Soil Science Society of America, 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Vadose Zone Journal 17 (2018): 170177, doi:10.2136/vzj2017.10.0177.

Description: Technological advances have allowed in situ monitoring of soil water content in an automated manner. These advances, along with an increase in large-scale networks monitoring soil water content, stress the need for a robust calibration framework that ensures that soil water content measurements are accurate and reliable. We have developed an approach to make consistent and comparable soil water content sensor calibrations across a continental-scale network in a production framework that incorporates a thorough accounting of uncertainties. More than 150 soil blocks of varying characteristics from 33 locations across the United States were used to generate soil-specific calibration coefficients for a capacitance sensor. We found that the manufacturer’s nominal calibration coefficients poorly fit the data for nearly all soil types. This resulted in negative (91% of samples) and positive (5% of samples) biases and a mean root mean square error (RMSE) of 0.123 cm3 cm−3 (1σ) relative to reference standard measurements. We derived soil-specific coefficients, and when used with the manufacturer’s nominal function, the biases were corrected and the mean RMSE dropped to ±0.017 cm3 cm−3 (±1σ). A logistic calibration function further reduced the mean RMSE to ±0.016 cm3 cm−3 (±1σ) and increased the range of soil moistures to which the calibration applied by 18% compared with the manufacturer’s function. However, the uncertainty of the reference standard was notable (±0.022 cm3 cm−3), and when propagated in quadrature with RMSE estimates, the combined uncertainty of the calibrated volumetric soil water content values increased to ±0.028 cm3 cm−3 regardless of the calibration function used.

Description: We acknowledge the National Science Foundation (NSF) for on-going support. NEON is a project sponsored by the NSF and managed under cooperative support agreement (EF-1029808) to Battelle.